




'''simple docstring'''


import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


UpperCAmelCase_					=   get_tests_dir("fixtures")





class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     int:
     # A mock response for an HTTP head request to emulate server down
     __a								=       mock.Mock()
     __a								=       500
     __a								=       {}
     __a								=       HTTPError
     __a								=       {}

     # Download this model to make sure it's in the cache.
     __a								=       WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'   )
     # Under the mock environment we get a 500 error when trying to reach the model.
     with mock.patch('requests.Session.request'   ,				return_value=UpperCamelCase   ) as mock_head:
       __a								=       WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'   )
       # This check we did call the fake head request
       mock_head.assert_called()







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     # This test is for deprecated behavior and can be removed in v5
     __a								=       WavaVecaFeatureExtractor.from_pretrained(
         'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'   )





@is_staging_test
class  __lowercase    (	unittest.TestCase			):







   @classmethod
   def 		UpperCamelCase__      (						cls   )					->     int:
     __a								=       TOKEN
     HfFolder.save_token(UpperCamelCase   )







   @classmethod
   def 		UpperCamelCase__      (						cls   )					->     Any:
     try:
       delete_repo(token=cls._token   ,				repo_id='test-feature-extractor'   )
     except HTTPError:
       pass

     try:
       delete_repo(token=cls._token   ,				repo_id='valid_org/test-feature-extractor-org'   )
     except HTTPError:
       pass

     try:
       delete_repo(token=cls._token   ,				repo_id='test-dynamic-feature-extractor'   )
     except HTTPError:
       pass







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase   )
     feature_extractor.push_to_hub('test-feature-extractor'   ,				use_auth_token=self._token   )

     __a								=       WavaVecaFeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor"   )
     for k, v in feature_extractor.__dict__.items():
       self.assertEqual(UpperCamelCase   ,				getattr(UpperCamelCase   ,				UpperCamelCase   )   )

     # Reset repo
     delete_repo(token=self._token   ,				repo_id='test-feature-extractor'   )

     # Push to hub via save_pretrained
     with tempfile.TemporaryDirectory() as tmp_dir:
       feature_extractor.save_pretrained(
           UpperCamelCase   ,				repo_id='test-feature-extractor'   ,				push_to_hub=UpperCamelCase   ,				use_auth_token=self._token   )

     __a								=       WavaVecaFeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor"   )
     for k, v in feature_extractor.__dict__.items():
       self.assertEqual(UpperCamelCase   ,				getattr(UpperCamelCase   ,				UpperCamelCase   )   )







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a								=       WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase   )
     feature_extractor.push_to_hub('valid_org/test-feature-extractor'   ,				use_auth_token=self._token   )

     __a								=       WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'   )
     for k, v in feature_extractor.__dict__.items():
       self.assertEqual(UpperCamelCase   ,				getattr(UpperCamelCase   ,				UpperCamelCase   )   )

     # Reset repo
     delete_repo(token=self._token   ,				repo_id='valid_org/test-feature-extractor'   )

     # Push to hub via save_pretrained
     with tempfile.TemporaryDirectory() as tmp_dir:
       feature_extractor.save_pretrained(
           UpperCamelCase   ,				repo_id='valid_org/test-feature-extractor-org'   ,				push_to_hub=UpperCamelCase   ,				use_auth_token=self._token   )

     __a								=       WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org'   )
     for k, v in feature_extractor.__dict__.items():
       self.assertEqual(UpperCamelCase   ,				getattr(UpperCamelCase   ,				UpperCamelCase   )   )







   def 		UpperCamelCase__      (						self   )					->     Dict:
     CustomFeatureExtractor.register_for_auto_class()
     __a								=       CustomFeatureExtractor.from_pretrained(UpperCamelCase   )

     feature_extractor.push_to_hub('test-dynamic-feature-extractor'   ,				use_auth_token=self._token   )

     # This has added the proper auto_map field to the config
     self.assertDictEqual(
         feature_extractor.auto_map   ,				{'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'}   ,				)

     __a								=       AutoFeatureExtractor.from_pretrained(
         f"{USER}/test-dynamic-feature-extractor"   ,				trust_remote_code=UpperCamelCase   )
     # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
     self.assertEqual(new_feature_extractor.__class__.__name__   ,				'CustomFeatureExtractor'   )









'''simple docstring'''


import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase_					=   {"LayoutLMv2Config", "LayoutLMv3Config"}





@is_pipeline_test
class  __lowercase    (	unittest.TestCase			):
   _a    						=   MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
   _a    						=   TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

   if model_mapping is not None:
      _a    						=   {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
   if tf_model_mapping is not None:
      _a    						=   {
          config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
      }







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       ZeroShotClassificationPipeline(
         model=UpperCamelCase   ,				tokenizer=UpperCamelCase   ,				candidate_labels=['polics', 'health']   )
     return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Tuple:
     __a								=       classifier('Who are you voting for in 2020?'   ,				candidate_labels='politics'   )
     self.assertEqual(UpperCamelCase   ,				{'sequence': ANY(UpperCamelCase   ), 'labels': [ANY(UpperCamelCase   )], 'scores': [ANY(UpperCamelCase   )]}   )

     # No kwarg
     __a								=       classifier('Who are you voting for in 2020?'   ,				['politics']   )
     self.assertEqual(UpperCamelCase   ,				{'sequence': ANY(UpperCamelCase   ), 'labels': [ANY(UpperCamelCase   )], 'scores': [ANY(UpperCamelCase   )]}   )

     __a								=       classifier('Who are you voting for in 2020?'   ,				candidate_labels=['politics']   )
     self.assertEqual(UpperCamelCase   ,				{'sequence': ANY(UpperCamelCase   ), 'labels': [ANY(UpperCamelCase   )], 'scores': [ANY(UpperCamelCase   )]}   )

     __a								=       classifier('Who are you voting for in 2020?'   ,				candidate_labels='politics, public health'   )
     self.assertEqual(
         UpperCamelCase   ,				{'sequence': ANY(UpperCamelCase   ), 'labels': [ANY(UpperCamelCase   ), ANY(UpperCamelCase   )], 'scores': [ANY(UpperCamelCase   ), ANY(UpperCamelCase   )]}   )
     self.assertAlmostEqual(sum(nested_simplify(outputs['scores']   )   )   ,				1.0   )

     __a								=       classifier('Who are you voting for in 2020?'   ,				candidate_labels=['politics', 'public health']   )
     self.assertEqual(
         UpperCamelCase   ,				{'sequence': ANY(UpperCamelCase   ), 'labels': [ANY(UpperCamelCase   ), ANY(UpperCamelCase   )], 'scores': [ANY(UpperCamelCase   ), ANY(UpperCamelCase   )]}   )
     self.assertAlmostEqual(sum(nested_simplify(outputs['scores']   )   )   ,				1.0   )

     __a								=       classifier(
         'Who are you voting for in 2020?'   ,				candidate_labels='politics'   ,				hypothesis_template='This text is about {}'   )
     self.assertEqual(UpperCamelCase   ,				{'sequence': ANY(UpperCamelCase   ), 'labels': [ANY(UpperCamelCase   )], 'scores': [ANY(UpperCamelCase   )]}   )

     # https://github.com/huggingface/transformers/issues/13846
     __a								=       classifier(['I am happy']   ,				['positive', 'negative']   )
     self.assertEqual(
         UpperCamelCase   ,				[
             {'sequence': ANY(UpperCamelCase   ), 'labels': [ANY(UpperCamelCase   ), ANY(UpperCamelCase   )], 'scores': [ANY(UpperCamelCase   ), ANY(UpperCamelCase   )]}
             for i in range(1   )
         ]   ,				)
     __a								=       classifier(['I am happy', 'I am sad']   ,				['positive', 'negative']   )
     self.assertEqual(
         UpperCamelCase   ,				[
             {'sequence': ANY(UpperCamelCase   ), 'labels': [ANY(UpperCamelCase   ), ANY(UpperCamelCase   )], 'scores': [ANY(UpperCamelCase   ), ANY(UpperCamelCase   )]}
             for i in range(2   )
         ]   ,				)

     with self.assertRaises(UpperCamelCase   ):
       classifier(''   ,				candidate_labels='politics'   )

     with self.assertRaises(UpperCamelCase   ):
       classifier(UpperCamelCase   ,				candidate_labels='politics'   )

     with self.assertRaises(UpperCamelCase   ):
       classifier('Who are you voting for in 2020?'   ,				candidate_labels=''   )

     with self.assertRaises(UpperCamelCase   ):
       classifier('Who are you voting for in 2020?'   ,				candidate_labels=UpperCamelCase   )

     with self.assertRaises(UpperCamelCase   ):
       classifier(
           'Who are you voting for in 2020?'   ,				candidate_labels='politics'   ,				hypothesis_template='Not formatting template'   ,				)

     with self.assertRaises(UpperCamelCase   ):
       classifier(
           'Who are you voting for in 2020?'   ,				candidate_labels='politics'   ,				hypothesis_template=UpperCamelCase   ,				)

     self.run_entailment_id(UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Optional[int]:
     __a								=       zero_shot_classifier.model.config
     __a								=       config.labelaid
     __a								=       zero_shot_classifier.entailment_id

     __a								=       {'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
     self.assertEqual(zero_shot_classifier.entailment_id   ,				-1   )

     __a								=       {'entailment': 0, 'neutral': 1, 'contradiction': 2}
     self.assertEqual(zero_shot_classifier.entailment_id   ,				0   )

     __a								=       {'ENTAIL': 0, 'NON-ENTAIL': 1}
     self.assertEqual(zero_shot_classifier.entailment_id   ,				0   )

     __a								=       {'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
     self.assertEqual(zero_shot_classifier.entailment_id   ,				2   )

     __a								=       original_labelaid
     self.assertEqual(UpperCamelCase   ,				zero_shot_classifier.entailment_id   )







   @require_torch
   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a								=       pipeline(
         'zero-shot-classification'   ,				model='sshleifer/tiny-distilbert-base-cased-distilled-squad'   ,				framework='pt'   ,				)
     # There was a regression in 4.10 for this
     # Adding a test so we don't make the mistake again.
     # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
     zero_shot_classifier(
         'Who are you voting for in 2020?' * 100   ,				candidate_labels=['politics', 'public health', 'science']   )







   @require_torch
   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       pipeline(
         'zero-shot-classification'   ,				model='sshleifer/tiny-distilbert-base-cased-distilled-squad'   ,				framework='pt'   ,				)
     __a								=       zero_shot_classifier(
         'Who are you voting for in 2020?'   ,				candidate_labels=['politics', 'public health', 'science']   )

     self.assertEqual(
         nested_simplify(UpperCamelCase   )   ,				{
             'sequence': 'Who are you voting for in 2020?',
             'labels': ['science', 'public health', 'politics'],
             'scores': [0.333, 0.333, 0.333],
         }   ,				)







   @require_tf
   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       pipeline(
         'zero-shot-classification'   ,				model='sshleifer/tiny-distilbert-base-cased-distilled-squad'   ,				framework='tf'   ,				)
     __a								=       zero_shot_classifier(
         'Who are you voting for in 2020?'   ,				candidate_labels=['politics', 'public health', 'science']   )

     self.assertEqual(
         nested_simplify(UpperCamelCase   )   ,				{
             'sequence': 'Who are you voting for in 2020?',
             'labels': ['science', 'public health', 'politics'],
             'scores': [0.333, 0.333, 0.333],
         }   ,				)







   @slow
   @require_torch
   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       pipeline('zero-shot-classification'   ,				model='roberta-large-mnli'   ,				framework='pt'   )
     __a								=       zero_shot_classifier(
         'Who are you voting for in 2020?'   ,				candidate_labels=['politics', 'public health', 'science']   )

     self.assertEqual(
         nested_simplify(UpperCamelCase   )   ,				{
             'sequence': 'Who are you voting for in 2020?',
             'labels': ['politics', 'public health', 'science'],
             'scores': [0.976, 0.015, 0.009],
         }   ,				)
     __a								=       zero_shot_classifier(
         'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
         ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
         ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
         ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
         ' machine translation tasks show these models to be superior in quality while being more parallelizable'
         ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
         ' English-to-German translation task, improving over the existing best results, including ensembles by'
         ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
         ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
         ' fraction of the training costs of the best models from the literature. We show that the Transformer'
         ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
         ' large and limited training data.'   ,				candidate_labels=['machine learning', 'statistics', 'translation', 'vision']   ,				multi_label=UpperCamelCase   ,				)
     self.assertEqual(
         nested_simplify(UpperCamelCase   )   ,				{
             'sequence': (
                 'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
                 ' networks in an encoder-decoder configuration. The best performing models also connect the'
                 ' encoder and decoder through an attention mechanism. We propose a new simple network'
                 ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
                 ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
                 ' superior in quality while being more parallelizable and requiring significantly less time to'
                 ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
                 ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
                 ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
                 ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
                 ' costs of the best models from the literature. We show that the Transformer generalizes well to'
                 ' other tasks by applying it successfully to English constituency parsing both with large and'
                 ' limited training data.'
             ),
             'labels': ['translation', 'machine learning', 'vision', 'statistics'],
             'scores': [0.817, 0.713, 0.018, 0.018],
         }   ,				)







   @slow
   @require_tf
   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       pipeline('zero-shot-classification'   ,				model='roberta-large-mnli'   ,				framework='tf'   )
     __a								=       zero_shot_classifier(
         'Who are you voting for in 2020?'   ,				candidate_labels=['politics', 'public health', 'science']   )

     self.assertEqual(
         nested_simplify(UpperCamelCase   )   ,				{
             'sequence': 'Who are you voting for in 2020?',
             'labels': ['politics', 'public health', 'science'],
             'scores': [0.976, 0.015, 0.009],
         }   ,				)
     __a								=       zero_shot_classifier(
         'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
         ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
         ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
         ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
         ' machine translation tasks show these models to be superior in quality while being more parallelizable'
         ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
         ' English-to-German translation task, improving over the existing best results, including ensembles by'
         ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
         ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
         ' fraction of the training costs of the best models from the literature. We show that the Transformer'
         ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
         ' large and limited training data.'   ,				candidate_labels=['machine learning', 'statistics', 'translation', 'vision']   ,				multi_label=UpperCamelCase   ,				)
     self.assertEqual(
         nested_simplify(UpperCamelCase   )   ,				{
             'sequence': (
                 'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
                 ' networks in an encoder-decoder configuration. The best performing models also connect the'
                 ' encoder and decoder through an attention mechanism. We propose a new simple network'
                 ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
                 ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
                 ' superior in quality while being more parallelizable and requiring significantly less time to'
                 ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
                 ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
                 ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
                 ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
                 ' costs of the best models from the literature. We show that the Transformer generalizes well to'
                 ' other tasks by applying it successfully to English constituency parsing both with large and'
                 ' limited training data.'
             ),
             'labels': ['translation', 'machine learning', 'vision', 'statistics'],
             'scores': [0.817, 0.713, 0.018, 0.018],
         }   ,				)



